




"""simple docstring"""







from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE_	     =       '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


SCREAMING_SNAKE_CASE_	     =       '\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


SCREAMING_SNAKE_CASE_	     =       '\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	, _KWARGS_DESCRIPTION			)
class 				snake_case_			(					datasets.Metric			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   Tuple:
       return datasets.MetricInfo(
           description=_DESCRIPTION       ,					citation=_CITATION       ,					inputs_description=_KWARGS_DESCRIPTION       ,					features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''float'''),
                   '''references''': datasets.Value('''float'''),
               })       ,					reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']       ,					)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_=False)				->   Any:
       if return_pvalue:
           UpperCamelCase              =	pearsonr(lowerCamelCase_       ,					lowerCamelCase_)
           return {"pearsonr": results[0], "p-value": results[1]}
       else:
           return {"pearsonr": float(pearsonr(lowerCamelCase_       ,					lowerCamelCase_)[0])}
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation


def 	lowerCamelCase					(	UpperCamelCase     :				List[str]							)   ->    Optional[Any]:
		_lowerCamelCase				   =  3_84
		_lowerCamelCase				   =  7
		if "tiny" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 6, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "small" in model_name:
				_lowerCamelCase				   =  96
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (3, 6, 12, 24)
		elif "base" in model_name:
				_lowerCamelCase				   =  1_28
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (4, 8, 16, 32)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  5_12
		elif "large" in model_name:
				_lowerCamelCase				   =  1_92
				_lowerCamelCase				   =  (2, 2, 18, 2)
				_lowerCamelCase				   =  (6, 12, 24, 48)
				_lowerCamelCase				   =  12
				_lowerCamelCase				   =  7_68

		# set label information
		_lowerCamelCase				   =  1_50
		_lowerCamelCase				   =  'huggingface/label-files'
		_lowerCamelCase				   =  'ade20k-id2label.json'
		_lowerCamelCase				   =  json.load(open(hf_hub_download(UpperCamelCase			,       UpperCamelCase			,       repo_type='dataset'							)			,       'r'							)							)
		_lowerCamelCase				   =  {int(UpperCamelCase							): v for k, v in idalabel.items()}
		_lowerCamelCase				   =  {v: k for k, v in idalabel.items()}

		_lowerCamelCase				   =  SwinConfig(
		    embed_dim=UpperCamelCase			,       depths=UpperCamelCase			,       num_heads=UpperCamelCase			,       window_size=UpperCamelCase			,       out_features=['stage1', 'stage2', 'stage3', 'stage4']			,       )
		_lowerCamelCase				   =  UperNetConfig(
		    backbone_config=UpperCamelCase			,       auxiliary_in_channels=UpperCamelCase			,       num_labels=UpperCamelCase			,       idalabel=UpperCamelCase			,       labelaid=UpperCamelCase			,       )

		return config


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  []

		# fmt: off
		# stem
		rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')							)
		rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')							)
		rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')							)
		rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')							)
		# stages
		for i in range(len(config.backbone_config.depths							)							):
				for j in range(config.backbone_config.depths[i]							):
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")							)

				if i < 3:
						rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")							)
						rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")							)
				rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")							)
				rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")							)

		# decode head
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]							)
		# fmt: on

		return rename_keys


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				Any			,       UpperCamelCase     :				Any							)   ->    str:
		_lowerCamelCase				   =  dct.pop(UpperCamelCase							)
		_lowerCamelCase				   =  val


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Tuple							)   ->    str:
		_lowerCamelCase				   =  [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
		for i in range(len(backbone_config.depths							)							):
				_lowerCamelCase				   =  num_features[i]
				for j in range(backbone_config.depths[i]							):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""							)
						_lowerCamelCase				   =  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""							)
						# next, add query, keys and values (in that order) to the state dict
						_lowerCamelCase				   =  in_proj_weight[:dim, :]
						_lowerCamelCase				   =  in_proj_bias[: dim]
						_lowerCamelCase				   =  in_proj_weight[
						    dim : dim * 2, :
						]
						_lowerCamelCase				   =  in_proj_bias[
						    dim : dim * 2
						]
						_lowerCamelCase				   =  in_proj_weight[
						    -dim :, :
						]
						_lowerCamelCase				   =  in_proj_bias[-dim :]
						# fmt: on


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Optional[int]:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3], :].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]							)   ->    int:
		_lowerCamelCase   ,							_lowerCamelCase				   =  x.shape
		_lowerCamelCase				   =  x.reshape(UpperCamelCase			,       in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, :, [0, 2, 1, 3]].transpose(1			,       2							).reshape(UpperCamelCase			,       UpperCamelCase							)

		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    List[str]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(4			,       in_channel // 4							)
		_lowerCamelCase				   =  x[[0, 2, 1, 3], :].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  x.shape[0]
		_lowerCamelCase				   =  x.reshape(in_channel // 4			,       4							)
		_lowerCamelCase				   =  x[:, [0, 2, 1, 3]].transpose(0			,       1							).reshape(UpperCamelCase							)
		return x


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Dict							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  {
		    'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
		    'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
		    'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
		    'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
		}
		_lowerCamelCase				   =  model_name_to_url[model_name]
		_lowerCamelCase				   =  torch.hub.load_state_dict_from_url(UpperCamelCase			,       map_location='cpu'			,       file_name=UpperCamelCase							)[
		    'state_dict'
		]

		for name, param in state_dict.items():
				print(UpperCamelCase			,       param.shape							)

		_lowerCamelCase				   =  get_upernet_config(UpperCamelCase							)
		_lowerCamelCase				   =  UperNetForSemanticSegmentation(UpperCamelCase							)
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				_lowerCamelCase				   =  state_dict.pop(UpperCamelCase							)
				if "bn" in key:
						_lowerCamelCase				   =  key.replace('bn'			,       'batch_norm'							)
				_lowerCamelCase				   =  val

		# rename keys
		_lowerCamelCase				   =  create_rename_keys(UpperCamelCase							)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
		read_in_q_k_v(UpperCamelCase			,       config.backbone_config							)

		# fix downsample parameters
		for key, value in state_dict.items():
				if "downsample" in key:
						if "reduction" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_reduction_order(UpperCamelCase							)
						if "norm" in key:
								_lowerCamelCase				   =  reverse_correct_unfold_norm_order(UpperCamelCase							)

		model.load_state_dict(UpperCamelCase							)

		# verify on image
		_lowerCamelCase				   =  'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
		_lowerCamelCase				   =  Image.open(requests.get(UpperCamelCase			,       stream=UpperCamelCase							).raw							).convert('RGB'							)

		_lowerCamelCase				   =  SegformerImageProcessor()
		_lowerCamelCase				   =  processor(UpperCamelCase			,       return_tensors='pt'							).pixel_values

		with torch.no_grad():
				_lowerCamelCase				   =  model(UpperCamelCase							)
				_lowerCamelCase				   =  outputs.logits

		print(logits.shape							)
		print('First values of logits:'			,       logits[0, 0, :3, :3]							)
		# assert values
		if model_name == "upernet-swin-tiny":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]							)
		elif model_name == "upernet-swin-small":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]							)
		elif model_name == "upernet-swin-base":
				_lowerCamelCase				   =  torch.tensor(
				    [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]							)
		elif model_name == "upernet-swin-large":
				_lowerCamelCase				   =  torch.tensor(
				    [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]							)
		print('Logits:'			,       outputs.logits[0, 0, :3, :3]							)
		assert torch.allclose(outputs.logits[0, 0, :3, :3]			,       UpperCamelCase			,       atol=1e-4							)
		print('Looks ok!'							)

		if pytorch_dump_folder_path is not None:
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(UpperCamelCase							)
				print(F"""Saving processor to {pytorch_dump_folder_path}"""							)
				processor.save_pretrained(UpperCamelCase							)

		if push_to_hub:
				print(F"""Pushing model and processor for {model_name} to hub"""							)
				model.push_to_hub(F"""openmmlab/{model_name}"""							)
				processor.push_to_hub(F"""openmmlab/{model_name}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='upernet-swin-tiny',
				    type=str,
				    choices=[F'''upernet-swin-{size}''' for size in ['tiny', 'small', 'base', 'large']],
				    help='Name of the Swin + UperNet model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
				)

				A						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)